用神经网络+PI控制提升外骨骼蹲起的实时性与鲁棒性
A Novel Robust Control Method Combining DNN-Based NMPC Approximation and PI Control: Application to Exoskeleton Squat Movements
- 用DNN近似NMPC并融合PI控制，实现快速响应
- 未知工况下跟踪误差显著降低，计算成本减少99.93%
- 适合外骨骼等复杂人机系统，减轻人体关节受力
非线性模型预测控制（NMPC）精度高，但计算负担重，难以在机器人系统中应用。已有研究尝试用深度神经网络（NMPC-DNN）近似NMPC，但在未见干扰或工况偏离训练数据时，其鲁棒性差，导致跟踪误差大。为此，首次提出将NMPC-DNN输出与PI控制器结合的混合方法（Hybrid NMPC-DNN-PI）。该控制器在具有三个主动关节（踝、膝、髋）的人-机器人动态模型上验证，使用超过530万组训练样本训练DNN。结果表明，在未见过的条件下，混合控制器的跟踪误差显著低于纯NMPC-DNN；同时，外骨骼有效降低人体关节扭矩，踝、膝、髋的均方根值分别下降30.9%、41.8%和29.7%。此外，混合控制器的计算开销比原NMPC降低99.93%。
原文摘要 · Abstract (English)
Nonlinear Model Predictive Control (NMPC) is a precise controller, but its heavy computational load often prevents application in robotic systems. Some studies have attempted to approximate NMPC using deep neural networks (NMPC-DNN). However, in the presence of unexpected disturbances or when operating conditions differ from training data, this approach lacks robustness, leading to large tracking errors. To address this issue, for the first time, the NMPC-DNN output is combined with a PI controller (Hybrid NMPC-DNN-PI). The proposed controller is validated by applying it to an exoskeleton robot during squat movement, which has a complex dynamic model and has received limited attention regarding robust nonlinear control design. A human-robot dynamic model with three active joints (ankle, knee, hip) is developed, and more than 5.3 million training samples are used to train the DNN. The results show that, under unseen conditions for the DNN, the tracking error in Hybrid NMPC-DNN-PI is significantly lower compared to NMPC-DNN. Moreover, human joint torques are greatly reduced with the use of the exoskeleton, with RMS values for the studied case reduced by 30.9%, 41.8%, and 29.7% at the ankle, knee, and hip, respectively. In addition, the computational cost of Hybrid NMPC-DNN-PI is 99.93% lower than that of NMPC.
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